CVE-2026-5080
Predictable Session ID Generation in Dancer::Session::Abstract for Perl
Publication date: 2026-04-30
Last updated on: 2026-05-05
Assigner: CPANSec
Description
Description
CVSS Scores
EPSS Scores
| Probability: | |
| Percentile: |
Meta Information
Affected Vendors & Products
| Vendor | Product | Version / Range |
|---|---|---|
| perldancer | dancer | to 1.3522 (inc) |
Helpful Resources
Exploitability
| CWE ID | Description |
|---|---|
| CWE-338 | The product uses a Pseudo-Random Number Generator (PRNG) in a security context, but the PRNG's algorithm is not cryptographically strong. |
| CWE-340 | The product uses a scheme that generates numbers or identifiers that are more predictable than required. |
Attack-Flow Graph
AI Powered Q&A
Can you explain this vulnerability to me?
CVE-2026-5080 is a vulnerability in Dancer::Session::Abstract versions through 1.3522 for Perl where session IDs are generated insecurely.
The session ID generation method combines predictable elements such as the absolute pathname of the application, the process ID, the epoch time, and calls to the built-in rand() function. These factors are either guessable or come from a small range of values, making the session IDs predictable.
Because the rand() function is seeded with only 32 bits and is not suitable for security purposes, the resulting session IDs can be predicted by attackers.
This predictability allows attackers to potentially guess valid session IDs and gain unauthorized access to systems.
How can this vulnerability impact me? :
This vulnerability can allow an attacker to predict session IDs due to weak randomness in their generation.
If an attacker can guess or predict valid session IDs, they may be able to hijack user sessions and gain unauthorized access to the application or system.
Such unauthorized access can lead to data breaches, unauthorized actions within the application, and compromise of user privacy and security.
How can this vulnerability be detected on my network or system? Can you suggest some commands?
This vulnerability involves predictable session IDs generated by Dancer::Session::Abstract versions through 1.3522 for Perl. Detection involves identifying if your system is running a vulnerable version of the Dancer framework and analyzing session IDs for predictability.
You can check the version of the Dancer module installed on your system using Perl commands such as:
- perl -MDancer -e 'print $Dancer::VERSION . "\n";'
To detect predictable session IDs, you can capture session cookies from HTTP traffic and analyze their structure and entropy. For example, using tcpdump or Wireshark to capture HTTP headers containing session cookies, then examining if session IDs appear to be repeated or predictable.
- tcpdump -i <interface> -A -s 0 'tcp port 80 and (((ip[2:2] - ((ip[0]&0xf)<<2)) - ((tcp[12]&0xf0)>>2)) != 0)'
- Use scripts or tools to analyze session ID randomness and check if session IDs can be guessed based on known installation paths, process IDs, or timestamps.
What immediate steps should I take to mitigate this vulnerability?
The immediate mitigation step is to update the Dancer::Session::Abstract module to a version that includes the patch for CVE-2026-5080.
The patch replaces the insecure session ID generation method with a cryptographically secure random number generator, Crypt::SysRandom::random_bytes(16), which produces unpredictable 16-byte session IDs.
If updating is not immediately possible, consider implementing additional layers of session security such as regenerating session IDs frequently, restricting session cookie scope, and monitoring for suspicious session activity.
How does this vulnerability affect compliance with common standards and regulations (like GDPR, HIPAA)?:
The vulnerability in Dancer::Session::Abstract results in predictable session IDs due to weak randomness in their generation. Predictable session IDs could allow attackers to hijack user sessions, potentially leading to unauthorized access to sensitive data.
Such unauthorized access risks violating data protection requirements in common standards and regulations like GDPR and HIPAA, which mandate appropriate security measures to protect personal and sensitive information.
Therefore, this vulnerability could negatively impact compliance by failing to ensure the confidentiality and integrity of user sessions, which are critical for protecting personal data under these regulations.